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How MATLAB makes the distinction between P-Cores and E-Cores?

  It is known that modern CPUs have both Performance cores (P-cores) and efficiency cores (E-cores), different types of CPU cores that have different purposes and are designed for different tasks. P-cores typically have higher clock speeds and designed for high-performance tasks, while E-cores operate at lower clock speeds and focus on energy-efficient processing. In MATLAB, maxNumCompThreads returns the current maximum number of computational threads. Currently, the maximum number of computational threads is equal to the number of physical cores on your machine. How MATLAB makes the distinction between P-Cores and E-Cores ? NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects...
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What are some good alternatives to Simulink?

Matlabsolutions provide latest  MatLab Homework Help, MatLab Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. SIMULINK is a visual programing environment specially for time transient simulations and ordinary differential equations. Depending on what you need there are plenty of Free, Libre and Open Source Software (FLOSS) available: Modelica language is the most viable alternative and in my opinion it is also a superior option to MathWorks SIMULINK. There are open source implementations  OpenModelica  and  JModelica . One of the main advantages with Modelica that you can code a multidimensional ordinary differential equation with algebraic discrete non-causal equations. With OpenModelica you may create a non-causal model r...
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USING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR DETECTING SPAM AND NON-SPAM EMAILS

    ABSTRACT We know the increasing volume of unwanted volume of emails as spam. As per statistical analysis 40% of all messages are spam which about 15.4 billion email for every day and that cost web clients about $355 million every year. Spammers to use a few dubious techniques to defeat the filtering strategies like utilizing irregular sender addresses or potentially add irregular characters to the start or the finish of the message subject line. A particular calculation is at that point used to take in the order rules from these email messages. Machine learning has been contemplated and there are loads of calculations can be used in email filtering. To classify these mails as spam and non-spam mails implementation of machine learning algorithm  such as KNN, SVM, Bayesian classification  and ANN  to develop better filtering tool.   Contents ABSTRACT 2 1. INTRODUCTION 4 1.1 Objective : 5 2. Literature Review 5 2.1. Existing Machine learning techniqu...
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